Laser powder bed fusion of nickel alloy 625: Experimental investigations of effects of process parameters on melt pool size and shape with spatter analysis

Author(s):  
Luis E. Criales ◽  
Yiğit M. Arısoy ◽  
Brandon Lane ◽  
Shawn Moylan ◽  
Alkan Donmez ◽  
...  
Author(s):  
Christopher U Brown ◽  
Gregor Jacob ◽  
Antonio Possolo ◽  
Carlos Beauchamp ◽  
Max Peltz ◽  
...  

Author(s):  
Yong Ren ◽  
Qian Wang ◽  
Panagiotis (Pan) Michaleris

Abstract Laser powder bed fusion (L-PBF) additive manufacturing (AM) is one type of metal-based AM process that is capable of producing high-value complex components with a fine geometric resolution. As melt-pool characteristics such as melt-pool size and dimensions are highly correlated with porosity and defects in the fabricated parts, it is crucial to predict how process parameters would affect the melt-pool size and dimensions during the build process to ensure the build quality. This paper presents a two-level machine learning (ML) model to predict the melt-pool size during the scanning of a multi-track build. To account for the effect of thermal history on melt-pool size, a so-called (pre-scan) initial temperature is predicted at the lower-level of the modeling architecture, and then used as a physics-informed input feature at the upper-level for the prediction of melt-pool size. Simulated data sets generated from the Autodesk's Netfabb Simulation are used for model training and validation. Through numerical simulations, the proposed two-level ML model has demonstrated a high prediction performance and its prediction accuracy improves significantly compared to a naive one-level ML without using the initial temperature as an input feature.


Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1856
Author(s):  
Claudia Schwerz ◽  
Lars Nyborg

In situ monitoring of the melt pools in laser powder bed fusion (LPBF) has enabled the elucidation of process phenomena. There has been an increasing interest in also using melt pool monitoring to identify process anomalies and control the quality of the manufactured parts. However, a better understanding of the variability of melt pools and the relation to the incidence of internal flaws are necessary to achieve this goal. This study aims to link distributions of melt pool dimensions to internal flaws and signal characteristics obtained from melt pool monitoring. A process mapping approach is employed in the manufacturing of Hastelloy X, comprising a vast portion of the process space. Ex situ measurements of melt pool dimensions and analysis of internal flaws are correlated to the signal obtained through in situ melt pool monitoring in the visible and near-infrared spectra. It is found that the variability in melt pool dimensions is related to the presence of internal flaws, but scatter in melt pool dimensions is not detectable by the monitoring system employed in this study. The signal intensities are proportional to melt pool dimensions, and the signal is increasingly dynamic following process conditions that increase the generation of spatter.


2017 ◽  
Vol 13 ◽  
pp. 14-36 ◽  
Author(s):  
Luis E. Criales ◽  
Yiğit M. Arısoy ◽  
Brandon Lane ◽  
Shawn Moylan ◽  
Alkan Donmez ◽  
...  

2019 ◽  
Vol 116 ◽  
pp. 83-91 ◽  
Author(s):  
Ali Keshavarzkermani ◽  
Ehsan Marzbanrad ◽  
Reza Esmaeilizadeh ◽  
Yahya Mahmoodkhani ◽  
Usman Ali ◽  
...  

2016 ◽  
Vol 25 (8) ◽  
pp. 3390-3397 ◽  
Author(s):  
Christopher U. Brown ◽  
Gregor Jacob ◽  
Mark Stoudt ◽  
Shawn Moylan ◽  
John Slotwinski ◽  
...  

2021 ◽  
Vol 6 (1) ◽  
pp. 2
Author(s):  
Mohamed Balbaa ◽  
Mohamed Elbestawi

Laser powder bed fusion exhibits many advantages for manufacturing complex geometries from hard to machine alloys such as IN625. However, a major drawback is the formation of high tensile residual stresses, and the complex relationship between the process parameters and the residual stresses has not been fully investigated. The current study presents multi-scale models to examine the variation of process parameters on melt pool dimensions, cyclic temperature evolutions, cooling rate, and cyclic stress generation and how they affect the stress end state. In addition, the effect of the same energy density, which is often overlooked, on the generated residual stresses is investigated. Multi-level validation is performed based on melt pool dimensions, temperature measurements with a two-color pyrometer, and finally, in-depth residual stress measurement. The results show that scan speed has the strongest effect on residual stresses, followed by laser power and hatch spacing. The results are explained in light of the non-linear temperature evolution, temperature gradient, and cooling rate during laser exposure, cooling time, and the rate during recoating time.


Metals ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 87
Author(s):  
Faiyaz Ahsan ◽  
Jafar Razmi ◽  
Leila Ladani

The powder bed fusion additive manufacturing process has received widespread interest because of its capability to manufacture components with a complicated design and better surface finish compared to other additive techniques. Process optimization to obtain high quality parts is still a concern, which is impeding the full-scale production of materials. Therefore, it is of paramount importance to identify the best combination of process parameters that produces parts with the least defects and best features. This work focuses on gaining useful information about several features of the bead area, such as contact angle, porosity, voids, melt pool size and keyhole that were achieved using several combinations of laser power and scan speed to produce single scan lines. These features are identified and quantified using process learning, which is then used to conduct a comprehensive statistical analysis that allows to estimate the effect of the process parameters, such as laser power and scan speed on the output features. Both single and multi-response analyses are applied to analyze the response parameters, such as contact angle, porosity and melt pool size individually as well as in a collective manner. Laser power has been observed to have a more influential effect on all the features. A multi-response analysis showed that 150 W of laser power and 200 mm/s produced a bead with the best possible features.


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